Wind Energy Science
Wind Energy Science
WES
Articles | Volume 10, issue 1
Articles | Volume 10, issue 1
https://doi.org/10.5194/wes-10-143-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-10-143-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 10, issue 1
Research article
 | 
15 Jan 2025
Research article |  | 15 Jan 2025

Characterization of local wind profiles: a random forest approach for enhanced wind profile extrapolation

Farkhondeh (Hanie) Rouholahnejad and Julia Gottschall

Viewed

Total article views: 1,626 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,272 314 40 1,626 53 59
  • HTML: 1,272
  • PDF: 314
  • XML: 40
  • Total: 1,626
  • BibTeX: 53
  • EndNote: 59
Views and downloads (calculated since 05 Feb 2024)
Cumulative views and downloads (calculated since 05 Feb 2024)

Viewed (geographical distribution)

Total article views: 1,626 (including HTML, PDF, and XML) Thereof 1,575 with geography defined and 51 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 07 Aug 2025
Download
Short summary
In wind energy, precise wind speed prediction at hub height is vital. Our study in the Dutch North Sea reveals that the on-site-trained random forest model outperforms the global reanalysis data, ERA5, in accuracy and precision. Trained within a 200 km range, the model effectively extends the wind speed vertically but experiences bias. It also outperforms ERA5 corrected with measurements in capturing wind speed variations and fine wind patterns, highlighting its potential for site assessment.
Read more
Share
Altmetrics
Final-revised paper
Preprint